The LISA space observatory, scheduled for launch in 2035, will consist of three satellites 2.5 million kilometers apart and will allow the direct detection of gravitational waves undetectable by terrestrial interferometers, opening a new window of observations in astrophysics. In order to maximize the scientific potential of such a mission, the analysis of the data will involve several steps, one of which is the rapid analysis pipeline, whose role is the detection of new events, as well as the characterization of events. Beyond the interest for LISA, this low latency analysis pipeline plays a key role for the fast follow-up of events detected by electromagnetic observations (ground or space observatories, from radio waves to Gamma rays). If fast analysis methods have been developed for ground-based interferometers, the case of space-based interferometers such as LISA remains a field to explore. Thus, an adapted data processing will have to take into account the mode of transmission of the data by packet, thus requiring the detection of events from incomplete data, marred by artifacts. These methods will have to allow the detection and characterization of events as diverse as black hole mergers, EMRIs (extreme mass ratio inspirals), bursts and binaries of compact objects. All this must be done in real time. To this end, this thesis will aim at generalizing classical methods, based on matched filtering, to the analysis of LISA data and at developing a new approach based on machine learning for the detection and early characterization of black hole mergers. These methods will be done in the framework of the LISA consortium and will contribute to the development of a fast analysis pipeline in France.